Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets
Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets
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综合数据集中直接方法和基于凯氏定氮方法的总氮数据比较
DOI：
10.1002/lom3.10338
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Collins, Sarah M.
中科院分区：
文献类型：
--
作者：
Stanley, Emily H.;Rojas‐Salazar, Shirley;Lottig, Noah R.;Schliep, Erin M.;Filstrup, Christopher T.;Collins, Sarah M.
测定水中总氮（TN）有多种方法，但大多数可以分为直接方法（TN‐d），将N形式转化为氮氧化物（NOx），以及结合凯氏定氮（有机N +NH3）和亚硝酸盐+硝酸盐（NO2+NO3‐N）的组合方法（TN‐c）。尽管这两种方法的化学性质不同，但在使用来自多个来源（即综合数据集）的数据的研究中，这两种方法的TN浓度通常被视为相等。我们使用两个综合数据集来确定TN - c和TN - d结果是否可互换。准确度由报告浓度与参考样品的最可能值（MPV）之间的差异决定，两种方法的准确度都很高，而且在量级上相似（在MPV的3.5-4.5%范围内），尽管低浓度的TN‐d偏差明显较小。检测限和标记为以下检测的数据表明，一个数据集对TN‐d的灵敏度更高，而另一个数据集的模式则不明确。虽然TN‐d数据包含一小部分明显不准确的结果，但TN‐c的结果在许多测量中变化更大（精度更低）。TN‐c的精度进一步受到传播误差的影响，除非完整的元数据可用并被检查，否则在集成数据集中可能无法确认或检测到。最后，湖泊样品中同时测量的TN - c和TN - d非常相似。总体而言，TN‐d倾向于稍微更准确和精确，但准确度的相似性以及同时测量TN‐d和TN‐c的接近1:1的关系支持在分析异构集成数据集时谨慎地互换使用数据。
There are multiple protocols for determining total nitrogen (TN) in water, but most can be grouped into direct approaches (TN‐d) that convert N forms to nitrogen‐oxides (NOx) and combined approaches (TN‐c) that combine Kjeldahl N (organic N +NH3) and nitrite+nitrate (NO2+NO3‐N). TN concentrations from these two approaches are routinely treated as equal in studies that use data derived from multiple sources (i.e., integrated data sets) despite the distinct chemistries of the two methods. We used two integrated data sets to determine if TN‐c and TN‐d results were interchangeable. Accuracy, determined as the difference between reported concentrations and the most probable value (MPV) of reference samples, was high and similar in magnitude (within 3.5–4.5% of the MPV) for both methods, although the bias was significantly smaller at low concentrations for TN‐d. Detection limits and data flagged as below detection suggested greater sensitivity for TN‐d for one data set, while patterns from the other data set were ambiguous. TN‐c results were more variable (less precise) by many measures, although TN‐d data included a small fraction of notably inaccurate results. Precision of TN‐c was further compromised by propagated error, which may not be acknowledged or detectable in integrated data sets unless complete metadata are available and inspected. Finally, concurrent measures of TN‐c and TN‐d in lake samples were extremely similar. Overall, TN‐d tended to be slightly more accurate and precise, but similarities in accuracy and the near 1 : 1 relationship for concurrent TN‐d and TN‐c measurements support careful use of data interchangeably in analyses of heterogeneous, integrated data sets.
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